Last checked: 31-Jan-2022
21 participants (experimental error = 1) >> 20 participants were included for analysis.
Actual instruction:
Each performance was produced in order to either 1) teach the musical expressive technique (as a teacher) or 2) perform their best (as a performer).
You will be asked to judge whether each performer had the intention to teach or not by pressing the 'Yes' <Left> or 'No' <Right> key.
***: 0.001, **: 0.01, *: 0.05## `geom_smooth()` using formula 'y ~ x'
##
## Shapiro-Wilk normality test
##
## data: ioi[Skill == "articulation"]$Teaching
## W = 0.97385, p-value = 0.5398
##
## Shapiro-Wilk normality test
##
## data: ioi[Skill == "articulation"]$Mean
## W = 0.94184, p-value = 0.05796
##
## Pearson's product-moment correlation
##
## data: ioi[Skill == "articulation"]$Teaching and ioi[Skill == "articulation"]$Mean
## t = 1.4838, df = 34, p-value = 0.1471
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08915582 0.53203453
## sample estimates:
## cor
## 0.2466031
##
## Shapiro-Wilk normality test
##
## data: ioi[Skill == "dynamics"]$Teaching
## W = 0.93654, p-value = 0.03968
##
## Shapiro-Wilk normality test
##
## data: ioi[Skill == "dynamics"]$Mean
## W = 0.94622, p-value = 0.07951
##
## Pearson's product-moment correlation
##
## data: ioi[Skill == "dynamics"]$Teaching and ioi[Skill == "dynamics"]$Mean
## t = 2.4731, df = 34, p-value = 0.01855
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07104344 0.63725327
## sample estimates:
## cor
## 0.3904656
##
## Spearman's rank correlation rho
##
## data: ioi[Skill == "dynamics"]$Teaching and ioi[Skill == "dynamics"]$Mean
## S = 5549.6, p-value = 0.09111
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2857712
## `geom_smooth()` using formula 'y ~ x'
##
## Shapiro-Wilk normality test
##
## data: ioi_tra[Skill == "articulation"]$Teaching
## W = 0.97385, p-value = 0.5398
##
## Shapiro-Wilk normality test
##
## data: ioi_tra[Skill == "articulation"]$Mean
## W = 0.86402, p-value = 0.000407
##
## Pearson's product-moment correlation
##
## data: ioi_tra[Skill == "articulation"]$Teaching and ioi_tra[Skill == "articulation"]$Mean
## t = -0.21462, df = 34, p-value = 0.8313
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3609560 0.2953224
## sample estimates:
## cor
## -0.0367821
##
## Spearman's rank correlation rho
##
## data: ioi_tra[Skill == "articulation"]$Teaching and ioi_tra[Skill == "articulation"]$Mean
## S = 7366, p-value = 0.7633
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.05198897
##
## Shapiro-Wilk normality test
##
## data: ioi_tra[Skill == "dynamics"]$Teaching
## W = 0.93654, p-value = 0.03968
##
## Shapiro-Wilk normality test
##
## data: ioi_tra[Skill == "dynamics"]$Mean
## W = 0.68463, p-value = 1.637e-07
##
## Pearson's product-moment correlation
##
## data: ioi_tra[Skill == "dynamics"]$Teaching and ioi_tra[Skill == "dynamics"]$Mean
## t = 3.0482, df = 34, p-value = 0.004434
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1589380 0.6872201
## sample estimates:
## cor
## 0.4632826
##
## Spearman's rank correlation rho
##
## data: ioi_tra[Skill == "dynamics"]$Teaching and ioi_tra[Skill == "dynamics"]$Mean
## S = 4873.1, p-value = 0.02513
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3728308
## `geom_smooth()` using formula 'y ~ x'
##
## Shapiro-Wilk normality test
##
## data: cv[Skill == "articulation"]$Teaching
## W = 0.97385, p-value = 0.5398
##
## Shapiro-Wilk normality test
##
## data: cv[Skill == "articulation"]$CV
## W = 0.72145, p-value = 6.255e-07
##
## Pearson's product-moment correlation
##
## data: cv[Skill == "articulation"]$Teaching and cv[Skill == "articulation"]$CV
## t = -1.1432, df = 34, p-value = 0.261
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4899546 0.1453349
## sample estimates:
## cor
## -0.1923869
##
## Spearman's rank correlation rho
##
## data: cv[Skill == "articulation"]$Teaching and cv[Skill == "articulation"]$CV
## S = 8455.3, p-value = 0.609
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.0882002
##
## Shapiro-Wilk normality test
##
## data: cv[Skill == "dynamics"]$Teaching
## W = 0.93654, p-value = 0.03968
##
## Shapiro-Wilk normality test
##
## data: cv[Skill == "dynamics"]$CV
## W = 0.68893, p-value = 1.904e-07
##
## Pearson's product-moment correlation
##
## data: cv[Skill == "dynamics"]$Teaching and cv[Skill == "dynamics"]$CV
## t = 1.3111, df = 34, p-value = 0.1986
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1176402 0.5110755
## sample estimates:
## cor
## 0.2193741
##
## Spearman's rank correlation rho
##
## data: cv[Skill == "dynamics"]$Teaching and cv[Skill == "dynamics"]$CV
## S = 6261.9, p-value = 0.2567
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1940868
## `geom_smooth()` using formula 'y ~ x'
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Legato"]$Teaching
## W = 0.97385, p-value = 0.5398
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Legato"]$Mean
## W = 0.97859, p-value = 0.6976
##
## Pearson's product-moment correlation
##
## data: kot_all[Subcomponent == "Legato"]$Teaching and kot_all[Subcomponent == "Legato"]$Mean
## t = -0.15232, df = 34, p-value = 0.8798
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3516330 0.3050387
## sample estimates:
## cor
## -0.02611412
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Staccato"]$Teaching
## W = 0.97385, p-value = 0.5398
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Staccato"]$Mean
## W = 0.96129, p-value = 0.2355
##
## Pearson's product-moment correlation
##
## data: kot_all[Subcomponent == "Staccato"]$Teaching and kot_all[Subcomponent == "Staccato"]$Mean
## t = -0.87009, df = 34, p-value = 0.3904
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4541034 0.1901707
## sample estimates:
## cor
## -0.1475859
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Forte"]$Teaching
## W = 0.93654, p-value = 0.03968
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Forte"]$Mean
## W = 0.78286, p-value = 7.57e-06
##
## Pearson's product-moment correlation
##
## data: kot_all[Subcomponent == "Forte"]$Teaching and kot_all[Subcomponent == "Forte"]$Mean
## t = -0.64948, df = 34, p-value = 0.5204
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4238216 0.2260574
## sample estimates:
## cor
## -0.1106998
##
## Spearman's rank correlation rho
##
## data: kot_all[Subcomponent == "Forte"]$Teaching and kot_all[Subcomponent == "Forte"]$Mean
## S = 9115.5, p-value = 0.3125
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1731673
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Piano"]$Teaching
## W = 0.93654, p-value = 0.03968
##
## Shapiro-Wilk normality test
##
## data: kot_all[Subcomponent == "Piano"]$Mean
## W = 0.89598, p-value = 0.002653
##
## Pearson's product-moment correlation
##
## data: kot_all[Subcomponent == "Piano"]$Teaching and kot_all[Subcomponent == "Piano"]$Mean
## t = -2.2047, df = 34, p-value = 0.03434
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.61118593 -0.02843273
## sample estimates:
## cor
## -0.3536648
##
## Spearman's rank correlation rho
##
## data: kot_all[Subcomponent == "Piano"]$Teaching and kot_all[Subcomponent == "Piano"]$Mean
## S = 10597, p-value = 0.02913
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.3638967
## `geom_smooth()` using formula 'y ~ x'
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Forte"]$Teaching
## W = 0.93654, p-value = 0.03968
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Forte"]$Mean
## W = 0.93909, p-value = 0.04761
##
## Pearson's product-moment correlation
##
## data: vel_all[Subcomponent == "Forte"]$Teaching and vel_all[Subcomponent == "Forte"]$Mean
## t = 2.8987, df = 34, p-value = 0.006517
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1365985 0.6749772
## sample estimates:
## cor
## 0.4451568
##
## Spearman's rank correlation rho
##
## data: vel_all[Subcomponent == "Forte"]$Teaching and vel_all[Subcomponent == "Forte"]$Mean
## S = 4460.3, p-value = 0.009591
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.425953
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Piano"]$Teaching
## W = 0.93654, p-value = 0.03968
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Piano"]$Mean
## W = 0.90118, p-value = 0.003677
##
## Pearson's product-moment correlation
##
## data: vel_all[Subcomponent == "Piano"]$Teaching and vel_all[Subcomponent == "Piano"]$Mean
## t = -2.9072, df = 34, p-value = 0.006378
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.6756828 -0.1378714
## sample estimates:
## cor
## -0.4461963
##
## Spearman's rank correlation rho
##
## data: vel_all[Subcomponent == "Piano"]$Teaching and vel_all[Subcomponent == "Piano"]$Mean
## S = 10972, p-value = 0.01252
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.4120633
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Legato"]$Teaching
## W = 0.97385, p-value = 0.5398
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Legato"]$Mean
## W = 0.94958, p-value = 0.1014
##
## Pearson's product-moment correlation
##
## data: vel_all[Subcomponent == "Legato"]$Teaching and vel_all[Subcomponent == "Legato"]$Mean
## t = 0.48723, df = 34, p-value = 0.6292
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2521651 0.4008392
## sample estimates:
## cor
## 0.08326912
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Staccato"]$Teaching
## W = 0.97385, p-value = 0.5398
##
## Shapiro-Wilk normality test
##
## data: vel_all[Subcomponent == "Staccato"]$Mean
## W = 0.9757, p-value = 0.5997
##
## Pearson's product-moment correlation
##
## data: vel_all[Subcomponent == "Staccato"]$Teaching and vel_all[Subcomponent == "Staccato"]$Mean
## t = 1.247, df = 34, p-value = 0.2209
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1282152 0.5030993
## sample estimates:
## cor
## 0.2091298
## `geom_smooth()` using formula 'y ~ x'
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "FtoP"]$Teaching
## W = 0.93654, p-value = 0.03968
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "FtoP"]$Mean
## W = 0.98328, p-value = 0.8496
##
## Pearson's product-moment correlation
##
## data: vel_diff_all[Subcomponent == "FtoP"]$Teaching and vel_diff_all[Subcomponent == "FtoP"]$Mean
## t = -6.5455, df = 34, p-value = 1.7e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.8634165 -0.5540757
## sample estimates:
## cor
## -0.7466889
##
## Spearman's rank correlation rho
##
## data: vel_diff_all[Subcomponent == "FtoP"]$Teaching and vel_diff_all[Subcomponent == "FtoP"]$Mean
## S = 13407, p-value = 5.563e-07
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.7254799
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "PtoF"]$Teaching
## W = 0.93654, p-value = 0.03968
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "PtoF"]$Mean
## W = 0.91318, p-value = 0.00798
##
## Pearson's product-moment correlation
##
## data: vel_diff_all[Subcomponent == "PtoF"]$Teaching and vel_diff_all[Subcomponent == "PtoF"]$Mean
## t = 4.2234, df = 34, p-value = 0.0001699
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3196656 0.7672660
## sample estimates:
## cor
## 0.5865961
##
## Spearman's rank correlation rho
##
## data: vel_diff_all[Subcomponent == "PtoF"]$Teaching and vel_diff_all[Subcomponent == "PtoF"]$Mean
## S = 3340.9, p-value = 0.0002843
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5700193
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "LtoS"]$Teaching
## W = 0.97385, p-value = 0.5398
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "LtoS"]$Mean
## W = 0.94562, p-value = 0.07613
##
## Pearson's product-moment correlation
##
## data: vel_diff_all[Subcomponent == "LtoS"]$Teaching and vel_diff_all[Subcomponent == "LtoS"]$Mean
## t = 1.3835, df = 34, p-value = 0.1755
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.105688 0.519962
## sample estimates:
## cor
## 0.2308638
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "StoL"]$Teaching
## W = 0.97385, p-value = 0.5398
##
## Shapiro-Wilk normality test
##
## data: vel_diff_all[Subcomponent == "StoL"]$Mean
## W = 0.95266, p-value = 0.1268
##
## Pearson's product-moment correlation
##
## data: vel_diff_all[Subcomponent == "StoL"]$Teaching and vel_diff_all[Subcomponent == "StoL"]$Mean
## t = 2.2252, df = 34, p-value = 0.03281
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03171544 0.61324012
## sample estimates:
## cor
## 0.3565362
pcor.test(partial[Subcomponent == "Legato"]$KOT, partial[Subcomponent == "Legato"]$Teaching, partial[Subcomponent == "Legato", c("IOI", "KV", "KVDiff")])
pcor.test(partial[Subcomponent == "Staccato"]$KOT, partial[Subcomponent == "Staccato"]$Teaching, partial[Subcomponent == "Staccato", c("IOI", "KV", "KVDiff")])
pcor.test(partial[Subcomponent == "Forte"]$KV, partial[Subcomponent == "Forte"]$Teaching, partial[Subcomponent == "Forte", c("IOI", "KOT", "KVDiff")])
pcor.test(partial[Subcomponent == "Piano"]$KV, partial[Subcomponent == "Piano"]$Teaching, partial[Subcomponent == "Piano", c("IOI", "KOT", "KVDiff")])
pcor.test(partial[Subcomponent2 == "FtoP"]$KVDiff, partial[Subcomponent2 == "FtoP"]$Teaching, partial[Subcomponent2 == "FtoP", c("IOI", "KOT", "KV")])
pcor.test(partial[Subcomponent2 == "PtoF"]$KVDiff, partial[Subcomponent2 == "PtoF"]$Teaching, partial[Subcomponent2 == "PtoF", c("IOI", "KOT", "KV")])
Note: additive - no interaction considered
m1 <- lm(Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == "Legato"])
summary(m1)
##
## Call:
## lm(formula = Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent ==
## "Legato"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.722 -10.706 1.336 9.958 24.511
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -184.31750 75.50965 -2.441 0.02056 *
## IOI 0.58277 0.21572 2.702 0.01109 *
## KOT -0.08497 0.11520 -0.738 0.46631
## KV 1.10473 0.44163 2.501 0.01786 *
## KVDiff 1.89934 0.64740 2.934 0.00625 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.89 on 31 degrees of freedom
## Multiple R-squared: 0.2894, Adjusted R-squared: 0.1977
## F-statistic: 3.156 on 4 and 31 DF, p-value: 0.02752
check_model(m1)
m2 <- lm(Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == "Staccato"])
summary(m2)
##
## Call:
## lm(formula = Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent ==
## "Staccato"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.617 -9.232 1.271 9.239 27.628
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -65.8795 70.5859 -0.933 0.3579
## IOI 0.1210 0.2993 0.404 0.6889
## KOT -0.1780 0.2030 -0.877 0.3873
## KV 0.5797 0.3986 1.454 0.1559
## KVDiff 0.7302 0.3755 1.945 0.0609 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.57 on 31 degrees of freedom
## Multiple R-squared: 0.2231, Adjusted R-squared: 0.1228
## F-statistic: 2.225 on 4 and 31 DF, p-value: 0.08915
check_model(m2)
m3 <- lm(Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == "Forte"])
summary(m3)
##
## Call:
## lm(formula = Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent ==
## "Forte"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.844 -7.451 -1.172 8.217 26.761
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -60.40467 80.97584 -0.746 0.461312
## IOI 0.28747 0.22992 1.250 0.220532
## KOT -0.10305 0.06189 -1.665 0.105986
## KV 0.02354 0.40267 0.058 0.953766
## KVDiff -1.63853 0.38204 -4.289 0.000163 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.69 on 31 degrees of freedom
## Multiple R-squared: 0.6518, Adjusted R-squared: 0.6069
## F-statistic: 14.51 on 4 and 31 DF, p-value: 8.785e-07
check_model(m3)
m4 <- lm(Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == "Piano"])
summary(m4)
##
## Call:
## lm(formula = Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent ==
## "Piano"])
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.022 -9.992 -1.424 9.291 24.718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -106.08277 89.12100 -1.190 0.242955
## IOI 0.55404 0.22918 2.417 0.021700 *
## KOT -0.06475 0.09523 -0.680 0.501636
## KV -0.65679 0.63689 -1.031 0.310404
## KVDiff 1.09161 0.24538 4.449 0.000104 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.38 on 31 degrees of freedom
## Multiple R-squared: 0.5526, Adjusted R-squared: 0.4949
## F-statistic: 9.574 on 4 and 31 DF, p-value: 3.681e-05
check_model(m4)